Papers Published

1. Meireles, OR; Rosman, G; Altieri, MS; Carin, L; Hager, G; Madani, A; Padoy, N; Pugh, CM; Sylla, P; Ward, TM; Hashimoto, DA; SAGES Video Annotation for AI Working Groups, SAGES consensus recommendations on an annotation framework for surgical video., Surg Endosc, vol. 35 no. 9 (September, 2021), pp. 4918-4929 [doi] .
   (last updated on 2024/12/31)

   Abstract:
   BACKGROUND: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. METHODS: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. RESULTS: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. CONCLUSIONS: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.